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Neural Architecture Search
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Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS essentially takes the process of a human manually tweaking a neural network and learning what works well, and automates this task to discover more complex architectures.
Image Credit : NAS with Reinforcement Learning
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/nikdnaik/accelerating_nas-1
Accelerating Neural Architecture Search using Performance Prediction
https://github.com/nikhilbarhate99/PPO
https://github.com/nikhilbarhate99/PPO-PyTorch
https://github.com/nikita270a/BayesianDARTS
https://github.com/nimiew/Grab-Computer-Vision
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/nitthilan/pommerman
https://github.com/nlinc1905/evolutionary-reinforcement-learner
https://github.com/northeastsquare/effficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/nric/ProximalPolicyOptimizationContinuousKeras
https://github.com/nsarang/MnasNet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/nutellamok/advrush
https://github.com/nvlabs/gbrl_sb3
https://github.com/ofsoundof/Heterogeneity_Hypothesis
https://github.com/open-edge-platform/geti
https://github.com/open-edge-platform/training_extensions
https://github.com/open-mmlab/mmclassification
https://github.com/open-mmlab/mmdetection
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
https://github.com/open-mmlab/mmpose
https://github.com/open-mmlab/mmrazor
https://github.com/openpsi-projects/srl
https://github.com/osmr/imgclsmob
Learning Transferable Architectures for Scalable Image Recognition
https://github.com/pdefraene/cgp_cnn_predictors
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures
https://github.com/pengboxiangshang/mobilenetv3_pytorch
https://github.com/peteryuX/pcdarts-tf2
PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search
https://github.com/phossen/autokeras
https://github.com/phykn/film-defect-detection
https://github.com/pikkaay/efficientnet_gpu
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/pipilurj/BONAS
https://github.com/pjlab-adg/openpcseg
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
https://github.com/pjlab-adg/pcseg
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
https://github.com/pnnl/torchntk
Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective
https://github.com/polarizationpruning/PolarizationPruning
Learning Efficient Convolutional Networks through Network Slimming
https://github.com/pprp/ofa-cifar
Once-for-All: Train One Network and Specialize it for Efficient Deployment
https://github.com/prakhargoyal106/MelanomaClassification
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/pwc-1/Paper-9/tree/main/6/GraphPAS
GraphPAS: Parallel Architecture Search for Graph Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.